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The efficacy of using judgmental versus quantitative forecasting methods in practice

Author

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  • Sanders, Nada R.
  • Manrodt, Karl B.

Abstract

In an era where forecasts drive entire supply chains forecasting is seen as an increasingly critical organizational capability. However, business forecasting continues to rely on judgmental methods despite large advancements in information technology and quantitative method capability, prompting calls for research to help understand the reasons behind this practice. Our study is designed to contribute to this knowledge by profiling differences between firms identified as primary users of either judgmental or quantitative forecasting methods. Relying on survey data from 240 firms we statistically analyzed differences between these categories of users based on a range of organizational and forecasting issues. Our study finds large differences in forecast error rates between the two groups, with users of quantitative methods significantly outperforming users of judgmental methods. The former are found to be equally prevalent regardless of industry, firm size, and product positioning strategy, documenting the benefits of quantitative method use in a variety of settings. By contrast, the latter are found to have significantly lower access to quantifiable data and to use information and technology to a lesser degree.

Suggested Citation

  • Sanders, Nada R. & Manrodt, Karl B., 2003. "The efficacy of using judgmental versus quantitative forecasting methods in practice," Omega, Elsevier, vol. 31(6), pages 511-522, December.
  • Handle: RePEc:eee:jomega:v:31:y:2003:i:6:p:511-522
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    References listed on IDEAS

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    1. Dalrymple, Douglas J., 1975. "Sales forecasting methods and accuracy," Business Horizons, Elsevier, vol. 18(6), pages 69-73, December.
    2. Mady, M. Tawfik, 2000. "Sales forecasting practices of Egyptian public enterprises: survey evidence," International Journal of Forecasting, Elsevier, vol. 16(3), pages 359-368.
    3. Fok, Dennis & Franses, Philip Hans, 2001. "Forecasting market shares from models for sales," International Journal of Forecasting, Elsevier, vol. 17(1), pages 121-128.
    4. Lawrence, Michael, 2000. "What does it take to achieve adoption in sales forecasting?," International Journal of Forecasting, Elsevier, vol. 16(2), pages 147-148.
    5. Lim, Joa Sang & O'Connor, Marcus, 1996. "Judgmental forecasting with time series and causal information," International Journal of Forecasting, Elsevier, vol. 12(1), pages 139-153, March.
    6. Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
    7. Adya, Monica & Armstrong, J. Scott & Collopy, Fred & Kennedy, Miles, 2000. "An application of rule-based forecasting to a situation lacking domain knowledge," International Journal of Forecasting, Elsevier, vol. 16(4), pages 477-484.
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